nextNEOpi: a comprehensive pipeline for computational neoantigen prediction

Area of Science:

• Computational biology
• Cancer immunology
• Bioinformatics

Background:

• Somatic mutations and gene fusions generate neoantigens that stimulate anticancer immune responses.
• Predicting these neoantigens computationally involves complex workflows to identify tumor-specific alterations, derive peptides, determine Human Leukocyte Antigen (HLA) types, and predict neoepitope binding and immunogenicity.

Purpose of the Study:

• To present nextNEOpi, a comprehensive and automated bioinformatics pipeline for predicting tumor neoantigens.
• To enable the prediction of neoantigens directly from raw DNA and RNA sequencing data.

Main Methods:

• nextNEOpi utilizes a Nextflow-based computational workflow.
• The pipeline automates the identification of tumor-specific aberrations, peptide derivation, HLA typing, and neoepitope prediction.
• It also quantifies patient- and neoepitope-specific features related to immunogenicity and immunotherapy response.

Main Results:

• nextNEOpi provides a fully automated solution for neoantigen prediction.
• The pipeline can process raw DNA and RNA sequencing data to identify potential neoantigens.
• It assesses features critical for predicting tumor immunogenicity and response to immunotherapy.

Conclusions:

• nextNEOpi streamlines the complex process of neoantigen discovery.
• This automated pipeline facilitates the identification of immunogenic neoantigens for cancer immunotherapy development.
• It offers valuable insights into patient-specific immune responses and treatment potential.